Patient monitoring and alarm processing system and user interface

ABSTRACT

Some embodiments of the invention provide a system to monitor a patient. In some aspects, a signal representing a value of a physiological parameter is received from a monitoring device. The received value is a value that caused the monitoring device to trigger an alarm associated with the psychological parameter. A notification is determined based on the received signal and on a second signal that represents a value of a second physiological parameter. The notification is then presented to an operator.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority to Provisional Application Ser. No.60/316,605, filed Aug. 31, 2001 and entitled “A Smart Alarm and DecisionSupport System.”

TECHNICAL FIELD

The present invention relates to medical systems and in particular tosystems for monitoring patient physiological parameters.

BACKGROUND

Patient treatment often includes monitoring of various physiologicalparameters. Conventionally, such monitoring begins by attaching sensorsto several locations on a patient's body. The sensors transmit signalsto one or more devices, which in turn determine the values of subjectparameters based on the signals. In this regard, a particular parametervalue may be determined based on a signal received from one or more ofthe attached sensors.

Most conventional monitoring devices include alarm functions. Generally,a device issues an audible and/or visual alarm after determining that aparticular parameter value has fallen outside a specified range. Thisarrangement presents numerous difficulties. First, a monitoring devicemay issue an alarm in a case that an associated sensor detaches from thepatient or in case of other non-emergency situations. Several monitoringdevices are often used to monitor a single patient, with each devicepresenting a possibility of a false alarm. These possibilities conditioncaregivers to reduce their sensitivity to the alarms.

A caregiver's decision-making processes are often prolonged duringcritical events due to a barrage of corresponding alarms from multiplemonitoring devices, some of which may be false alarms. Additionally, analarm usually fails to provide sufficient information to a caregiver.Most patient complications are evidenced by several physiologicalparameters, so an alarm-triggering value of one parameter isinsufficient to identify a particular complication.

In view of the foregoing, a more efficient system to process patientalarms is desired.

SUMMARY

Some embodiments of the invention provide a system to monitor a patient.In some aspects, a signal representing a value of a physiologicalparameter is received from a monitoring device. The received value is avalue that caused the monitoring device to trigger an alarm associatedwith the psychological parameter. A notification is determined based onthe received signal and on a second signal that represents a value of asecond physiological parameter. The notification is then presented to anoperator.

According to some aspects, probabilities associated with thenotification are presented to the operator. The probabilities indicate acalculated likelihood that the notification describes the patient'scondition. Some aspects also allow the operator to issue an instructionto suppress a particular notification, in response to which anotification is determined based on an assumption that the particularnotification is not indicative of the patient's condition.

The present invention should not be deemed limited to the disclosedembodiments, however, as those of ordinary skill in the art can readilyadapt the teachings of the present invention to create other embodimentsand applications.

BRIEF DESCRIPTION OF THE DRAWINGS

The exact nature of this invention, as well as its advantages, willbecome readily apparent from consideration of the followingspecification as illustrated in the accompanying drawings, wherein:

FIG. 1 is diagram illustrating patient monitoring according to someembodiments of the present invention;

FIG. 2 is a flow diagram illustrating process steps according to someembodiments of the present invention;

FIGS. 3 a and 3 b comprise expert-Bayesian networks for use inconjunction with some embodiments of the present invention; and

FIGS. 4 a and 4 b are outward views of displays presenting notificationsand associated probabilities according to some embodiments of thepresent invention.

DETAILED DESCRIPTION

The following description is provided to enable any person of ordinaryskill in the art to make and use the invention and sets forth the bestmodes contemplated by the inventor for carrying out the invention.Various modifications, however, will remain readily apparent to those inthe art.

FIG. 1 illustrates a patient monitoring system according to someembodiments of the present invention. The system illustrated in FIG. 1may be used in any number of locations and in any number of situations.Possible locations include a hospital, an office, and an ambulance, andpossible situations include during an operation, during a checkup, andduring a recovery period.

Attached to patient 1 are monitoring devices such as sensors forproducing signals associated with physiological parameters. The producedsignals are received by monitoring devices such as monitors fordetermining a value of a physiological parameter therefrom. Morespecifically, blood oxygen saturation level (SPO2) monitor 10 receives asignal associated with an SPO2 parameter from sensor 11,electrocardiogram (ECG) monitor 20 receives a signal associated with aheart rate (HR) parameter from sensor 21, respiration (RESP) monitor 30receives a signal associated with a RESP parameter from sensor 31, andinvasive blood pressure (IBP) monitor 40 receives a signal associatedwith an IBP parameter from sensor 41. Each of sensors 11, 21, 31 and 41is a sensor suitable to produce a signal representing an associatedparameter. Accordingly, each monitor is used to determine a value of anassociated parameter. In some embodiments, the signal received from SPO2monitor 10 may also be used to determine a value of a pulse (PLS)parameter.

It should be noted that, according to some embodiments, one or more ofmonitors 10 through 40 may receive signals from more than one sensorand/or two or more of monitors 10 through 40 may receive signals from asame sensor. In addition, each sensor may transmit a signal using anycurrently or hereafter-known system for transmitting data, including RF,infrared, or fiber-optic. Moreover, the signals may be transmitted overone or more of an IP network, an Ethernet network, a Bluetooth network,a cellular network, and any other suitable network.

Monitors 10 through 40 are in communication with communication bus 50.Again, communication bus 50 may comprise any type of network, andcommunication therewith may proceed in accordance with any hardwareand/or software protocol. It should be noted that the elements describedherein as being in communication with one another need not becontinuously exchanging data. Rather, in some embodiments, a connectionis established prior to each exchange of data and is severed thereafter.

Also in communication with communication bus 50 is alarm server 60.According to some embodiments, alarm server 60 operates to receive asignal representing a value of a physiological parameter, wherein thevalue has triggered an alarm associated with the psychologicalparameter, to determine a notification based at least on the receivedsignal and on a second signal representing a value of a secondphysiological parameter, and to present the notification to an operator.

In some embodiments, alarm server 60 includes a communication interfacefor receiving physiological parameter values from monitors 10 through40, a prediction unit for providing data representing an extrapolatedpatient condition based on the values, a decision processor for applyinga decision rule to the extrapolated patient condition to identify analert condition, and an alert generator for generating an alertindication in response to the alert condition. The prediction unit mayuse patient record information for extrapolating the patient condition.In some embodiments, the generator provides treatment and/or diagnosticsuggestions based on the extrapolated condition. The generator may alsoprovide user-selectable suggestion information of different degrees ofdetail with respect to the extrapolated condition, and may alsoassociate different reliability weighting with corresponding suggestioninformation of different degrees of detail.

More specifically, according to some embodiments, alarm server 60receives signals from monitors 10 through 40. Each of the signalsrepresents a value of a respective physiological parameter. At least oneof the values has triggered an alarm of its respective monitor becausethe value falls outside of a range of predetermined values. For example,SPO2 monitor 10 has been programmed to issue an alarm if it determinesan SPO2 value that is less than 80%. SPO2 monitor 10 receives a signalfrom sensor 11, determines an SPO2 value of 70% based on the signal, andissues an alarm because the determined value is less than 80%. SPO2monitor 10 transmits a signal representing the value to alarm server 60via communication bus 50. One or more of monitors 20 through 40 alsosends a signal representing a value of a physiological parameter toalarm server 60. The signals received from the one or more monitors mayor may not have triggered an alarm of their respective monitors. In thisregard, SPO2 monitor 10 may also transmit signals representing parametervalues that do not trigger an alarm to alarm server 60.

Alarm server 60 determines one or more notifications based on thereceived signals and presents the one or more notifications to anoperator. The notifications indicate a condition of patient 1 based onthe received signals. According to some embodiments, alarm server 60also determines a probability corresponding to each notification andpresents the probabilities in association with their correspondingnotifications. Systems for determining the notifications andprobabilities will be described in detail below.

In some embodiments, the operator can issue a command to suppress one ofthe presented notifications. In such a case, future reception of similarsignals would result in alarm server 60 determining subsequentnotifications that do not include the suppressed notification. Thesubsequent notifications are determined in some embodiments based on anassumption that the suppressed notification is not indicative of thepatient's condition. Suppression of certain notifications that are knownnot to describe a patient's condition may result in the determination ofmore reliable notifications and more accurate associated probabilities.Notification suppression according to some embodiments of the presentinvention is described below.

FIG. 2 is a flow diagram of process steps 200 according to someembodiments of the present invention. Although process steps 200 aredescribed as being performed by alarm server 60, hardware and/orsoftware for executing process steps 200 may be located in and/orexecuted by one or more of sensors 11 through 41, monitors 10 through40, and alarm server 60.

Initially, at step S201, alarm server 60 receives a first signal thatrepresents a value of a physiological parameter. The represented valuetriggered an alarm prior to step S201 because a device recognized thevalue as satisfying an alarm condition. The device may be a device thatdetermined the value, a device that issued the alarm, and/or anotherdevice. According to the present example, a signal is received in stepS201 from SPO2 monitor 10 that represents an SPO2 value of 70%. Thisvalue has triggered an alarm of SPO2 monitor 10 because the value isless than 80%.

Next, in step S202, alarm server 60 receives a second signal thatrepresents a value of a second parameter. The value of the secondparameter may or may not have triggered an alarm prior to step S202. Asignal representing a HR value of 110 is received from ECG monitor 20 inthe present example of step S202. Notifications and associatedprobabilities are determined in step S203 based on the first and secondsignals. It should be noted that the determination may be based onadditional signals representing values of additional physiologicalparameters. For example, the determination of the present example mayalso be based on a signal received from monitor 10 that represents a PLSvalue of 104.

The determined notifications are intended to describe the condition ofpatient 1, and the associated probabilities are intended to express alikelihood that the notification is accurate. Many systems can be usedto determine the notifications and the associated probabilities. Thesesystems may be based on one or more of rules, expert systems,probabilistic networks, predictive networks, neural networks, and thelike. According to a specific example, an expert-Bayesian network may beused in step S203.

One known method of creating an expert-Bayesian network begins with thecreation of an expert knowledge diagram. Generally, the diagramillustrates the approach an expert would take in making a determinationbased on various inputs. In the present instance, an expert knowledgemap associates physiological values with possible causes of the valuesvia probability distributions. Once an expert knowledge map is created,the probability distributions may be used to derive a correspondingexpert-Bayesian network. Known adaptive normalization techniques may beapplied to “train” the expert-Bayesian network based on its intendeduse.

FIG. 3 a illustrates an expert-Bayesian network for use in conjunctionwith some embodiments of step S203. The nodes of the two upper-mostlevels of the network determine probabilities associated withnotifications. The third level includes three nodes representingintermediate determinations that are made based on determinedphysiological parameters. Each node of the network includes amathematical function that determines a probability of particularscenarios based on the outputs of nodes from which the node receivesinput. In a specific example, the “dHR” node of FIG. 3 a may include thefollowing mathematical function, which, as shown in FIG. 3 a, operateson the outputs of the “Heart_Condition” and “ECG_Reliability” nodes: p(dHR|ECG_Reliability, Heart_Condition) = (ECG_Reliability == High &&Heart_Condition == Bradycardiac) ? NormalDist(dHR, −5, 2):(ECG_Reliability == High && Heart_Condition == Tachycardiac) ?NormalDist(dHR, 5, 2): (ECG_Reliability == High && Heart_Condition ==Normal) ? NormalDist(dHR, 0, 1): (ECG_Reliability == Low &&Heart_Condition == Bradycardiac) ? NormalDist(dHR, −5, 15):(ECG_Reliability == Low && Heart_Condition == Tachycardiac) ?NormalDist(dHR, 5, 15): (ECG_Reliability == Low && Heart_Condition ==Normal) ? NormalDist(dHR, 0, 20): 0

This function determines a probability table as shown below:Heart_Condition ECG_(—) Reliability Bradycardiac Tachycardiac NormalHigh n(dHR, −5, 2) n(dHR, 5, 2) n(dHR, 0, 1) Low n(dHR, −5, 15) n(dHR,5, 15) n(dHR, 0, 20)Accordingly, the data shown in cell (1, 1) indicates that in a statewhere ECG_Reliability = High and Heart_Condition = Bradycardiac, dHR maybe expressed by a normal distribution having mean = −5 and standarddeviation = 2.

It should be noted that the inputs to a node may themselves consist ofprobability distributions. For example, one node of the third levelrepresents a probability that a received RESP value has a Highreliability and a probability that the received RESP value has a Lowreliability based on a probability distribution of the derivative of theRESP value and a probability distribution of the standard deviation ofthe RESP value.

The grayed-out nodes of FIG. 3 a are not associated with probabilitydistributions because the actual values represented by the nodes areknown. According to the present example, the grayed-out nodes representvalues received by alarm server 60 in steps S201 and S202. Therefore,the probabilities reflected within the other nodes of FIG. 3 a are basedon the received values and on the mathematical functions included in thenodes. More particularly, based on the received values of 70% SPO2, 110HR and 104 PLS, the notifications and probabilities of the twoupper-most levels of the FIG. 3 a network are determined in step S203.It should be noted that only one notification and/or no probability isdetermined in some embodiments of step S203.

The notifications and the associated probabilities are presented to anoperator in step S204. FIG. 4 a is an outward view of interface 61 ofalarm server 60 for use in presenting the notifications and theassociated probabilities. Interface 61 of FIG. 4 a presentsnotifications and associated probabilities determined based on thenetwork of FIG. 3 a. As shown, all notifications and/or associatedprobabilities determined in step S203 need not be presented in stepS204. For example, presented in step S204 might be only thosenotifications associated with probabilities of greater than 20% and/orthose notifications describing a critical condition and that areassociated with a probability of greater than 50%.

Interface 61 also includes buttons 62 and 63 for selecting one of thedisplayed notifications, button 64 for delaying an alarm, and button 65for suppressing a selected notification. According to some embodiments,the alarm that was triggered by the value of the signal received in stepS201 continues throughout steps S201 through S205. An operator may issuean instruction to temporarily disable the alarm by pressing button 64.In this case, the instruction is detected in step S205 and flow proceedsto step S206, in which the alarm is temporarily disabled for a specifiedperiod. Any other alarms issued by monitoring devices 20 through 40 mayalso be disabled in step S206. The disabled alarms resume afterexpiration of the specified period.

The operator may issue an instruction to ignore a presented notificationby selecting the notification using buttons 62 and/or 63 and bythereafter pressing button 65. In some instances, the operator may issuesuch an instruction because the operator is confident that the selectednotification does not describe the condition of patient 1. According tothe present example, the operator selects the “SPO2 Sensor Problem”notification and presses button 65. The instruction is received in stepS205, and flow thereafter proceeds to step S207.

In step S207, it is assumed that the selected notification is notindicative of the patient's condition. Flow returns to step S203 tore-determine the notifications and associated probabilities based onthis assumption. FIG. 3 b illustrates this re-determination using theexpert-Bayesian network of FIG. 3 a. More particularly, the probabilityassociated with the “SPO2 Sensor Problem” notifications has been forcedto 0% and each other node has been recalculated based thereon and on theoriginal values of the SPO2, HR and PLS parameters.

The re-determined notifications and associated probabilities arepresented to the operator in step S204. FIG. 4 b is a view of interface61 presenting the re-determined notifications and probabilitiesaccording to some embodiments of the present invention. As shown, the“SPO2 Sensor Problem” notification has been suppressed.

Those in the art will appreciate that various adaptations andmodifications of the above-described embodiments can be configuredwithout departing from the scope and spirit of the invention. In someembodiments, functions attributed above to monitors 10 through 40 areperformed by a single monitoring unit, such as the Siemens InfinityPatient Monitoring System™. Some embodiments also incorporate thefunctions of alarm server 60 into the single monitoring unit.

Embodiments of the present invention may differ from the description ofprocess steps 200. For example, in some embodiments, no probability isdetermined in step S203 or presented in step S204. According to someembodiments, alarm server 60 may receive a signal from a monitoringdevice representing a value of a physiological parameter that is notassociated with any node of a probabilistic network used by alarm server60 but that triggered an alarm of the monitoring device. As a result,alarm server 60 presents a notification of the alarm on interface 61along with other notifications that are determined as described above.Such an arrangement allows alarm server 60 to handle alarms other thanthose alarms that it has been programmed to analyze in conjunction withother signals.

In some embodiments, the operator may also issue an instruction todiscontinue suppression of a notification. The particular arrangement ofprocess steps 200 is not meant to imply a fixed order to the steps;embodiments of the present invention can be practiced in any order thatis practicable. In this regard, flow may proceed from step S207 to stepS201 to determine notifications and/or probabilities based on a commandto suppress a notification and on newly-received parameter values.

Therefore, it is to be understood that, within the scope of the appendedclaims, the invention may be practiced other than as specificallydescribed herein.

1-17. (canceled)
 18. A medical alarm system for processing informationfrom a plurality of patient monitoring sources to generate an alertmessage, comprising: a communication interface for receiving patientphysiological parameter values from a plurality of patient monitoringdevices for a particular patient; a prediction unit for providing datarepresenting an extrapolated patient condition based on the parametervalues; a decision processor for applying a decision rule to saidextrapolated patient condition to identify an alert condition; and analert generator for generating an alert indication in response to saididentified alert condition.
 19. A system according to claim 18, whereinsaid prediction unit uses patient record information for a particularpatient in extrapolating patient condition.
 20. A medical alarm systemfor processing information from a plurality of patient monitoringsources to generate an alert message, comprising: a communicationinterface for receiving patient physiological parameters from aplurality of patient monitoring devices for a particular patient; aprediction unit for providing data representing an extrapolated patientcondition based on acquired patient medical parameters; and a generatorfor providing at least one of, (a) diagnostic and (b) treatmentsuggestions based on said extrapolated patient condition.
 21. A systemaccording to claim 20, wherein: said generator provides user-selectablesuggestion information of different degrees of detail concerning saidextrapolated condition.
 22. A system according to claim 21, wherein saidgenerator associates different reliability weighting to correspondingsuggestion information of different degrees of detail. 23-28. (canceled)